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Room-level indoor localization is of particular interest in the energy-efficient smart building, as services, such as lighting and ventilation, can be targeted towards individual rooms based on occupancy instead of an entire floor. Hence, this paper focuses on identifying the room where a person or a mobile device is physically present. Existing room-level localization methods, however, require special infrastructure to annotate rooms with special signatures. SoundLoc is a room-level localization scheme that exploits the intrinsic acoustic properties of individual rooms and obviates the needs for infrastructures. As we will show in the study, rooms' acoustic properties can be characterized by Room Impulse Response (RIR). Nevertheless, obtaining precise RIRs is a time-consuming and expensive process. The main contributions of our work are the following: First, a cost-effective RIR measurement system is designed and the Noise Adaptive Extraction of Reverberation (NAER) algorithm is developed to estimate room acoustic parameters in noisy conditions. Second, a comprehensive physical and statistical analysis of features extracted from RIRs is performed. Also, SoundLoc is evaluated using the dataset consisting of ten (10) different rooms and the overall accuracy of 97.8% has been achieved.
Jia et al. (Sat,) studied this question.